Implementation of a human papillomavirus screen-and-treat model in Mwanza, Tanzania: training local healthcare workers for sustainable impact.

Background: The purpose of this study is to evaluate the development of a 1-wk screening campaign and efforts towards the implementation of a sustainable system that addresses cervical cancer in Mwanza, Tanzania with a screen-and-treat model utilizing visual inspection of the cervix with acetic acid (VIA) and cryotherapy. Methods: In partnership with CureCervicalCancer (CCC), a non-profit organization based in Los Angeles, CA, USA, 11 medical students at the University of California, Irvine School of Medicine established a model for sustainable human papillomavirus screening practices in Mwanza, Tanzania. This study both quantitatively and qualitatively assesses the successes and limitations of the program model. Results: During the 5-day training, a total of 614 women attended the screenings and 556 women were screened with VIA, of whom 10.6% (n=59) were VIA positive and 89.4% (n=499) were VIA negative. Of those who were VIA positive, 83.1% (n=49) received cryotherapy while 16.9% (n=10) did not due to suspicion of advanced cancer (n=7), refusal to receive cryotherapy (n=2) or pregnancy (n=1). Conclusions: The screen-and-treat model for the identification and treatment of precancerous cervical lesions is an effective public health intervention with the potential to impact women by providing the tools and education needed by local healthcare professionals. However, limitations common to resource-poor settings, such as continuity of funding, loss to follow-up and transportation costs, remain barriers to sustainability.

Ultrasound curriculum taught by first-year medical students: A four-year experience in Tanzania.

BACKGROUND: Diagnostic imaging is an integral aspect of care that is often insufficient, if not altogether absent, in rural and remote regions of low to middle income countries (LMICs) such as Tanzania. The introduction of ultrasound can significantly impact treatment in these countries due to its portability, low cost, safety, and usefulness in various medical assessments. This study reviews the implementation of a four-week ultrasound course administered annually from 2013-2016 in a healthcare professional school in Mwanza, Tanzania by first-year allopathic US medical students. METHODS: Participants (n=582, over 4 years) were recruited from the Tandabui Institute of Health Sciences and Technology to take the ultrasound course. Subjects were predominantly clinical officer students, but other participants included other healthcare professional students, practicing healthcare professionals, and school employees. Data collected includes pre-course examination scores, post-course examination scores, course quiz scores, demographic surveys, and post-course feedback surveys. Data was analyzed using two-tailed t-tests and the single factor analysis of variance (ANOVA). RESULTS: For all participants who completed both the pre- and post-course examinations (n=229, 39.1% of the total recruited), there was a significant mean improvement in their ultrasound knowledge of 42.5%, P<0.01. CONCLUSION: Our data suggests that trained first-year medical students can effectively teach a point of care ultrasound course to healthcare professional students within four weeks in Tanzania. Future investigation into the level of long-term knowledge retention, impact of ultrasound training on knowledge of human anatomy and diagnostic capabilities, and how expansion of an ultrasound curriculum has impacted access to care in rural Tanzania is warranted.

Another Generation of Stigma? Assessing Healthcare Student Perceptions of HIV-Positive Patients in Mwanza, Tanzania.

HIV-related stigma remains a persistent global health concern among people living with HIV/AIDS (PLWA) in developing nations. The literature is lacking in studies about healthcare students' perceptions of PLWA. This study is the first effort to understand stigmatizing attitudes toward HIV-positive patients by healthcare students in Mwanza, Tanzania, not just those who will be directly treating patients but also those who will be indirectly involved through nonclinical roles, such as handling patient specimens and private health information. A total of 208 students were drawn from Clinical Medicine, Laboratory Sciences, Health Records and Information Management, and Community Health classes at the Tandabui Institute of Health Sciences and Technology for a voluntary survey that assessed stigmatizing beliefs toward PLWA. Students generally obtained high scores on the overall survey instrument, pointing to low stigmatizing beliefs toward PLWA and an overall willingness to treat PLWA with the same standard of care as other patients. However, there are gaps in knowledge that exist among students, such as a comprehensive understanding of all routes of HIV infection. The study also suggests that students who interact with patients as part of their training are less likely to exhibit stigmatizing beliefs toward PLWA. A comprehensive course in HIV infection, one that includes classroom sessions focused on the epidemiology and routes of transmission as well as clinical opportunities to directly interact with PLWA-perhaps through teaching sessions led by PLWA-may allow for significant reductions in stigma toward such patients and improve clinical outcomes for PLWA around the world.

Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.

The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data.

Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy.

The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.